A clinical decision support framework for automatic disease diagnoses

Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. A Clinical Decision Support System (CDS) can greatly help in identifying diseases and methods of treatment. In this paper we propose a CDS framework that can integrate heterogeneous health data from different sources, such as laboratory test results, basic information of patients, and health records. Using the electronic health medical data so collected, innovative machine learning and deep learning approaches are employed to implement a set of services to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients health issues more efficiently.